Brain Inspired
Paul Middlebrooks
Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.
Episodes
Mentioned books
4 snips
Jan 3, 2025 • 1h 38min
BI 202 Eli Sennesh: Divide-and-Conquer to Predict
In this engaging discussion, Eli Sennesh, a postdoctoral researcher at Vanderbilt University, sheds light on predictive coding and its implications for understanding brain functions. He navigates the intriguing concept of 'divide-and-conquer predictive coding' and its experimental applications. The conversation also touches on the relationship between neuroscience and AI, emphasizing the need for biologically plausible computational models. They explore the complexities of decision-making, consciousness, and the humor in our perceptions of task difficulty, offering a delightful blend of research insights and personal anecdotes.
Dec 18, 2024 • 1h 37min
BI 201 Rajesh Rao: From Predictive Coding to Brain Co-Processors
In this discussion, Rajesh Rao, a distinguished professor at the University of Washington, dives deep into the concept of predictive coding, revealing how our brains predict and adjust to sensory signals. He introduces his latest research on 'Active predictive coding,' expanding on how action and perception interplay in our cortical structures. The conversation also explores groundbreaking brain-computer interfaces, including BrainNet, which connects minds, and the ethical implications of augmenting human cognition through technology.
5 snips
Dec 4, 2024 • 1h 37min
BI 200 Grace Hwang and Joe Monaco: The Future of NeuroAI
Join Grace Hwang, a Program director at NIH specializing in neuroscience and AI, and Joe Monaco, a scientific program manager at NIH, as they dive deep into the future of NeuroAI. They discuss the BRAIN Initiative's successes, the integration of AI with neuroscience, and the significance of neurodynamical computing. Grace and Joe explore innovative concepts like digital twins and their implications for neurosurgery. They also address the need for interdisciplinary collaboration to tackle the complexities of brain research and the ethical dimensions of these advancements.
39 snips
Nov 26, 2024 • 1h 49min
BI 199 Hessam Akhlaghpour: Natural Universal Computation
Hessam Akhlaghpour, a postdoctoral researcher at Rockefeller University, delves into the theoretical realm of molecular computation and its intersections with neuroscience. He unveils fascinating insights on RNA's potential as a universal computational medium. The discussion spans the evolutionary significance of this capability and how combinatory logic plays a role in biological systems. Hessam reflects on how his journey through neuroscience reshaped his understanding of memory and computation, challenging traditional views and igniting new ideas in computational theories.
Nov 11, 2024 • 1h 35min
BI 198 Tony Zador: Neuroscience Principles to Improve AI
In this intriguing discussion, Tony Zador, head of the Zador lab at Cold Spring Harbor Laboratory, shares his insights on the synergy between neuroscience and artificial intelligence. He argues that biological principles can significantly improve AI efficiency, particularly through understanding animal behavior. The conversation dives into the evolution of NeuroAI, the pitfalls of current AI models, and the parallels between genetic coding and neural networks. Zador highlights the importance of incorporating developmental learning stages from humans and animals to create more adaptable AI systems.
18 snips
Oct 25, 2024 • 1h 30min
BI 197 Karen Adolph: How Babies Learn to Move and Think
In this insightful discussion, Karen Adolph, a professor at NYU and head of the Infant Action Lab, shares her groundbreaking research on how infants learn to move and think. Alongside her partner Mark Blumberg, they challenge traditional views of the motor cortex, revealing its role in processing sensory information rather than just motor functions. They dive into the importance of real-world observations in understanding development, the influence of ecological psychology, and how insights from infants can inform advances in artificial intelligence and robotics.
Oct 11, 2024 • 1h 20min
BI 196 Cristina Savin and Tim Vogels with Gaute Einevoll and Mikkel Lepperød
Cristina Savin, a neuroscientist studying learning through recurrent neural networks, and Tim Vogels, who explores synaptic plasticity using AI, join the conversation. They discuss the transformative impact of deep learning on neuroscience research and the balance between innovation and traditional scientific inquiry. The duo reflects on the challenges of staying diverse in methodologies while utilizing AI tools. They also humorously address the academic pressures of productivity and work-life balance, emphasizing the importance of interdisciplinary collaboration and broad reading in research.
Oct 8, 2024 • 1h 17min
BI 195 Ken Harris and Andreas Tolias with Gaute Einevoll and Mikkel Lepperød
Mikkel Lepperød, an organizer of the NeuroAI workshop, joins neuroscientists Ken Harris and Andreas Tolias to explore AI's influence on neuroscience. They delve into the intersection of neural modeling and AI, discussing the balance between predictive accuracy and interpretability. The conversation highlights the role of deep learning in understanding cognition, the potential pitfalls of AI in research, and the philosophical implications of modern models. They also share insights on validating scientific ideas and the evolving productivity landscape in academia.
Sep 27, 2024 • 1h 37min
BI 194 Vijay Namboodiri & Ali Mohebi: Dopamine Keeps Getting More Interesting
Vijay Namboodiri, who runs the Nam Lab at UCSF, teams up with Ali Mohebi, an assistant professor at UW-Madison, to dive deep into the intricacies of dopamine. They challenge the classic narrative of dopamine's role in reward prediction, proposing a retrospective view that redefines how we understand causal relationships. Their discussions cover sign tracking versus goal tracking in learning, the implications for addiction, and the need for new models that integrate temporal differences. They also touch on how our past experiences inform current decisions, shaping our understanding of learning.
4 snips
Sep 11, 2024 • 1h 33min
BI 193 Kim Stachenfeld: Enhancing Neuroscience and AI
Kim Stachenfeld, a Senior Research Scientist at Google DeepMind and a researcher at Columbia's Center for Theoretical Neuroscience, dives into the captivating world of neuroscience and AI. She discusses the critical role of neural networks in emulating human cognition and their applications in understanding the brain. Kim explores the nuances of reinforcement learning, the intersection of academia and industry, and insights into memory and intelligence. She also challenges traditional model hierarchies, emphasizing the need for predictive and interpretable models in AI.
